Intelligent Time Series Analysis for Intrusion Detection in the Internet of Things: A Generative-Adversarial-Network-Enhanced Convolutional-Neural-Network–Long-Short-Term-Memory Framework Using Signal Features
Himanshu Sharma, Prabhat Kumar, Kavita Sharma · Intelligent Computing · 2025
From smart cities to healthcare, the internet of things (IoT) has transformed numerous industries. However, this expansion has raised security concerns, particularly cyberattacks. Traditional IoT intrusion detection systems (IDSs) have high false-positive rates and low detection accuracy due to IoT devices and traffic patterns. To overcome these challenges, this research proposes an intelligent-computing-based time series IDS that utilizes sophisticated data augmentation, signal transformation, and deep learning methods. The system begins by augmenting minority-class samples using conditional generative adversarial networks to handle class imbalance. The augmented dataset is then transformed into signal representations based on mel frequency cepstral coefficients, allowing the model to capture both the frequency and temporal characteristics of network traffic. Finally, a hybrid convolutional-neural-network–long-short-term-memory (CNN–LSTM) architecture is trained to identify anomalous behaviors with enhanced accuracy and lower false-positive rates. The proposed model utilizes the Canadian Institute for Cybersecurity CICIoT2023 dataset, which is widely used for network security experiments. The results show that the proposed method outperforms conventional deep learning models in terms of accuracy, precision, and false-positive rate. Specifically, the proposed system improves accuracy by 5% to 10% across different attack types while reducing false-positive rates considerably. The research presents a detailed exploration of the advantages of signal transformation and explains how the CNN and LSTM models complement each other in detecting anomalies. This framework addresses the pressing need for intelligent time series analysis in cybersecurity through the introduction of a scalable and interpretable IDS solution specifically designed for IoT environments.